2 day course
This two day short course which has been designed to provide professionals in the environment sector with skills and knowledge in the application of statistics to aid the analysis of environmental problems.The objective of this course is to present a set of statistical techniques that can be used to analyse a range of problems. Statistical concepts will be explained and supported with real practical examples.
The first day of the course will cover the main statistical concepts with hands-on experience, illustrated by examples from real environmental problems.
08:30 - 9:00 Registration
09:00 - 09:15 Introduction to the course
09:15 - 10:30 Explore and understand your data
11:00 - 12:30 Identify statistical differences between groups of data
12:30 - 13:30 Lunch
13:30 - 15:00 Investigate relationships between quantitative variables, build models and make predictions
15:30 - 17:30 Practical session
The second day will take the form of a statistical workshop with practical case studies, demonstrating the application of a range of techniques to environmental data, including the topics covered during day one and also introducing more advanced statistical techniques such as principal component analysis.
09:00 - 12.30 Case Study one: How can I show a statistical significant difference between these datasets or varieties or treatments or scenarios? (use of ANOVA and t-test)
Case study two: How can I build a model to predict a target variable? (use of regression models)
12:30 - 13:30 Lunch
13:30 - 17.30 Case study three: How do I investigate the relationships between variables in large data sets? (use of principal component analysis).
The course is designed for environmental professionals who want to review and/or develop their statistical knowledge and use appropriate methods for analysing their data. On successful completion of this course the delegate will be able to:
- Decide which statistical method is most appropriate for the problems they encounter
- Apply the methods appropriately to investigate their data.
- Data cleaning, identification of outliers & extreme values (use of data transformation techniques)
- Simple and multiple linear regression
- General Linear Moldels (Analysis of Variance)
- Multivariate statistics (Principal Component Analysis).